Kevin Petrie
VP of Research at BARC US
Free study
Context Engineering for Agentic AI: Architecture, Use Cases, and Principles for Success
This BARC study, based on 285 global responses, defines context engineering, then explores why and how AI adopters can put it to work with an effective program.
What this BARC study answers for data & analytics leaders:
- How can AI leaders give agents the right business context, rules, and permissions without slowing innovation?
- What can context leaders teach the rest of the market about building reliable, scalable, and governed agentic AI?
- Which risks emerge when AI agents act on incomplete, outdated, or poorly governed context?
- Which challenges do organizations face when preparing diverse data and metadata for AI use?
- How can organizations start small, prove value quickly, and expand context engineering across the enterprise?
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Pietro Grammatico, Director Service Management BI, Vorwerk
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Further information
The report is based on a global survey of 285 data, AI, IT, and business stakeholders. It examines adoption trends, must-have characteristics, challenges, benefits, architectural requirements, and practical use cases in industries such as insurance and healthcare.
The note concludes with a practical path forward for organizations that want to reduce risk, increase efficiency, and create competitive differentiation through proprietary context.
Author(s)
Florian Bigelmaier
Analyst Data & Analytics
Florian Bigelmaier is an analyst with a focus on data management and data intelligence.
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This study shows leaders how to prepare enterprise data, metadata, and workflows for scalable agentic AI.